Climate and environmental changes are viewed among the most important risks in society at present. As the financial sector is key for the transition towards a low-carbon and more circular economy, financial institutions have to deal with climate-related and environmental financial risks (C&E risks). At the same time, the increased importance of these C&E risks also presents new business opportunities for the financial sector. Therefore, to support banks in their self-assessment and action plans, Zanders developed a Scan & Plan Solution on C&E risks.
According to Moore’s law, computing power doubles up each two years. This performance increase in computing power makes machine learning increasingly efficient each year, and widely applicable. But does this also apply to credit risk issues?
Machine learning (ML) models have already been around for decades. The exponential growth in computing power and data availability, however, has resulted in many new opportunities for ML models. One possible application is to use them in financial institutions’ risk management. This article gives a brief introduction of ML models, followed by the most promising opportunities for using ML models in financial risk management.
The low interest rate environment has faced banks with structural changes in customer behavior and converging products such as savings and current accounts. ING, one of Europe’s largest players in the savings market and a long-term client of Zanders, has positioned itself as one of the frontrunners in this environment. We sat down with Tom Tschirner (head of market risk at ING Germany) and Maarten Hummel (financial risk officer at ING Group) to gather their view on modeling and balance sheet management after these structural shifts.
This White Paper lays down the fundamentals and best practices for setting up a Model Risk Management Framework to help your organization to effectively deal with the operational- and regulatory challenges of increased complexity of quantitative models, processes, data and systems.